Abstract
Convolutional Neural Network Based Feature Extraction for IRIS Recognition
Highlights
In recent years, the concept of personal identity becomes critical, and the biometrics is a popular way for authentication, which has been considered as the most secure and hardest way for authentication purpose[1]
We propose an iris recognition system where the features are extracted from the pretrained Convolutional Neural Network (CNN) Alex-Net model, and for the classification task, the multi-class Support Vector Machine (SVM) is used
This paper evaluated the extracted learned features from a pre-trained Convolutional Neural Network (Alex-Net) followed by multi-class SVM algorithm to perform iris recognition
Summary
The concept of personal identity becomes critical, and the biometrics is a popular way for authentication, which has been considered as the most secure and hardest way for authentication purpose[1]. Among all unique physical features, iris biometrics is known as the most accurate and impossible to reproduce or replicate [3]. Extracting effective features is the major important stage in a lot of object recognition and computer vision tasks. Much attention is given to feature learning algorithms and Convolutional Neural Networks (CCN). In this algorithm, the image is fed directly to the convolutional neural networks, the algorithm extracts the best features of this image [6, 7]. We propose an iris recognition system where the features are extracted from the pretrained CNN Alex-Net model, and for the classification task, the multi-class Support Vector Machine (SVM) is used.
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